A Framework for Explainable Concept Drift Detection in Process Mining

Adams, Jan Niklas; Zelst, Sebastiaan J. van; Quack, Lara; Hausmann, Kathrin; Aalst, Wil M. P. van der; Rose, Thomas

doi:10.1007/978-3-030-85469-0_25

Cited by 19 publications

(5 citation statements)

References 18 publications

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“…In the same way, there were no major fluctuations associated with changes in waiting times. In the future research, the stability of the process offers the use of technique called concept drift (Adams et al 2021;Ceravolo et al 2020) and its applicability towards RPA, which needs to be examined further.…”

Section: Discussion Of the Ppafr With Regard To Business Process Simu...mentioning

confidence: 99%

The performance assessment framework (PPAFR) for RPA implementation in a loan application process using process mining

Šperka

Halaška

2022

Inf Syst E-Bus Manage

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When a company decides to automate its business processes by means of RPA (Robotic Process Automation), there are two fundamental questions that need to be answered. Firstly, what activities should the company automate and what characteristics make them suitable for RPA. The aim of the presented research is to design and demonstrate a data-driven performance framework assessing the impact of RPA implementation using process mining (PPAFR). Firstly, we comment on and summarise existing trends in process mining and RPA. Secondly, we describe research objectives and methods following the Design Science Research Methodology. Then, we identify critical factors for RPA implementation and design process stages of PPAFR. We demonstrate the design on real data from a loan application process. The demonstration consists of a process discovery using process mining methods, process analysis, and process simulation with assessment of RPA candidates. Based on the research results, a redesign of the process is proposed with emphasis on RPA implementation. Finally, we discuss the usefulness of PPAFR by helping companies to identify potentially suitable activities for RPA implementation and not overestimating potential gains. Obtained results show that within the loan application process, waiting times are the main causes of extended cases. If the waiting times are generated internally, it will be much easier for the company to address them. If the automation is focused mainly on processing times, the impact of automation on the overall performance of the process is insignificant or very low. Moreover, the research identified several characteristics which have to be considered when implementing RPA due to the impact on the overall performance of the process.

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Section: Discussion Of the Ppafr With Regard To Business Process Simu...mentioning

confidence: 99%

The performance assessment framework (PPAFR) for RPA implementation in a loan application process using process mining

Šperka

Halaška

2022

Inf Syst E-Bus Manage

View full text Add to dashboard Cite

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“…In particular, the definition of constraints and actions should be adapted accordingly such that they remain relevant in a specific situation. To this end, opportunities exist for fellow scholars to connect the detection, characterization, and explanation of concept drifts in business processes [48][49][50] to the dynamic and adaptive definition of constraints and actions. A possible direction is to deploy incremental learning and model adaptation [51] to ensure that the definition of constraints and actions are up-to-date with respect to evolving business processes.…”

Section: Discussionmentioning

confidence: 99%

Action-oriented process mining: bridging the gap between insights and actions

Park

Aalst

2022

Prog Artif Intell

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As business environments become more dynamic and complex, it becomes indispensable for organizations to objectively analyze business processes, monitor the existing and potential operational frictions, and take proactive actions to mitigate risks and improve performances. Process mining provides techniques to extract insightful knowledge of business processes from event data collected during the execution of the processes. Besides, various approaches have been suggested to support the real-time (predictive) monitoring of the process-related problems. However, the link between the insights from the continuous monitoring and the concrete management actions for the actual process improvement is missing. Action-oriented process mining aims at connecting the knowledge extracted from event data to actions. In this work, we propose a general framework for action-oriented process mining covering the continuous monitoring of operational processes and the automated execution of management actions. Based on the framework, we suggest a cube-based action engine where actions are generated by analyzing monitoring results in a multi-dimensional way. The framework is implemented as a ProM plug-in and evaluated by conducting experiments on both artificial and real-life information systems.

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“…However, one of the challenges is to find the right explanations and their representation for production processes. Furthermore, other AI approaches as, e.g., machine learning or process mining [3], need to be included in trustworthy explanations.…”

Section: Model-integrated Artificial Intelligence With Autonomous Age...mentioning

confidence: 99%

A Computer Science Perspective on Digital Transformation in Production

Brauner

Dalibor

Jarke

et al. 2022

ACM Trans. Internet Things

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The Industrial Internet-of-Things (IIoT) promises significant improvements for the manufacturing industry by facilitating the integration of manufacturing systems by Digital Twins. However, ecological and economic demands also require a cross-domain linkage of multiple scientific perspectives from material sciences, engineering, operations, business, and ergonomics, as optimization opportunities can be derived from any of these perspectives. To extend the IIoT to a true Internet of Production , two concepts are required: first, a complex, interrelated network of Digital Shadows which combine domain-specific models with data-driven AI methods; and second, the integration of a large number of research labs, engineering, and production sites as a World Wide Lab which offers controlled exchange of selected, innovation-relevant data even across company boundaries. In this article, we define the underlying Computer Science challenges implied by these novel concepts in four layers: Smart human interfaces provide access to information that has been generated by model-integrated AI . Given the large variety of manufacturing data, new data modeling techniques should enable efficient management of Digital Shadows, which is supported by an interconnected infrastructure . Based on a detailed analysis of these challenges, we derive a systematized research roadmap to make the vision of the Internet of Production a reality.

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A Framework for Explainable Concept Drift Detection in Process Mining

Cited by 19 publications

References 18 publications

The performance assessment framework (PPAFR) for RPA implementation in a loan application process using process mining

The performance assessment framework (PPAFR) for RPA implementation in a loan application process using process mining

Action-oriented process mining: bridging the gap between insights and actions

A Computer Science Perspective on Digital Transformation in Production

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